scanpy.external.tl.phenograph¶

scanpy.external.tl.
phenograph
(data, *, k=30, directed=False, prune=False, min_cluster_size=10, jaccard=True, primary_metric='euclidean', n_jobs= 1, q_tol=0.001, louvain_time_limit=2000, nn_method='kdtree')¶ PhenoGraph clustering [Levine15].
 Parameters
 data :
ndarray
,spmatrix
Union
[ndarray
,spmatrix
] Array of data to cluster or sparse matrix of knearest neighbor graph. If ndarray, nbyd array of n cells in d dimensions, if sparse matrix, nbyn adjacency matrix.
 k :
int
int
(default:30
) Number of nearest neighbors to use in first step of graph construction.
 directed :
bool
bool
(default:False
) Whether to use a symmetric (default) or asymmetric (“directed”) graph. The graph construction process produces a directed graph, which is symmetrized by one of two methods (see below).
 prune :
bool
bool
(default:False
) Whether to symmetrize by taking the average (
prune=False
) or product (prune=True
) between the graph and its transpose. min_cluster_size :
int
int
(default:10
) Cells that end up in a cluster smaller than min_cluster_size are considered outliers and are assigned to 1 in the cluster labels.
 jaccard :
bool
bool
(default:True
) If
True
, use Jaccard metric between kneighborhoods to build graph. IfFalse
, use a Gaussian kernel. primary_metric : {‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’}, {‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’}
Union
[Literal
[‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’],Literal
[‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘correlation’, ‘dice’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’]] (default:'euclidean'
) Distance metric to define nearest neighbors. Note that performance will be slower for correlation and cosine.
 n_jobs :
int
int
(default:1
) Nearest Neighbors and Jaccard coefficients will be computed in parallel using
n_jobs
. Ifn_jobs=1
, it is determined automatically. q_tol :
float
float
(default:0.001
) Tolerance (i.e., precision) for monitoring modularity optimization.
 louvain_time_limit :
int
int
(default:2000
) Maximum number of seconds to run modularity optimization. If exceeded the best result so far is returned.
 nn_method : {‘kdtree’, ‘brute’}
Literal
[‘kdtree’, ‘brute’] (default:'kdtree'
) Whether to use brute force or kdtree for nearest neighbor search. For very large highdimensional data sets, brute force (with parallel computation) performs faster than kdtree.
 data :
 Return type
Tuple
[ndarray
,spmatrix
,float
]Tuple
[ndarray
,spmatrix
,float
] Returns
Example
>>> from anndata import AnnData >>> import scanpy as sc >>> import scanpy.external as sce >>> import numpy as np >>> import pandas as pd
Assume adata is your annotated data which has the normalized data.
Then do PCA:
>>> sc.tl.pca(adata, n_comps = 100)
Compute phenograph clusters:
>>> result = sce.tl.phenograph(adata.obsm['X_pca'], k = 30)
Embed the phenograph result into adata as a categorical variable (this helps in plotting):
>>> adata.obs['pheno'] = pd.Categorical(result[0])
Check by typing “adata” and you should see under obs a key called ‘pheno’.
Now to show phenograph on tSNE (for example):
Compute tSNE:
>>> sc.tl.tsne(adata, random_state = 7)
Plot phenograph clusters on tSNE:
>>> sc.pl.tsne(adata, color = ['pheno'], s = 100, palette = sc.pl.palettes.vega_20_scanpy, legend_fontsize = 10)
Cluster and cluster centroids for input Numpy ndarray
>>> df = np.random.rand(1000,40) >>> df.shape (1000, 40) >>> result = sce.tl.phenograph(df, k=50) Finding 50 nearest neighbors using minkowski metric and 'auto' algorithm Neighbors computed in 0.16141605377197266 seconds Jaccard graph constructed in 0.7866239547729492 seconds Wrote graph to binary file in 0.42542195320129395 seconds Running Louvain modularity optimization After 1 runs, maximum modularity is Q = 0.223536 After 2 runs, maximum modularity is Q = 0.235874 Louvain completed 22 runs in 1.5609488487243652 seconds PhenoGraph complete in 2.9466471672058105 seconds
New results can be pushed into adata object:
>>> dframe = pd.DataFrame(data=df, columns=range(df.shape[1]),index=range(df.shape[0]) ) >>> adata = AnnData( X=dframe, obs=dframe, var=dframe) >>> adata.obs['pheno'] = pd.Categorical(result[0])